DataRobot-Research/drdocs huggingface dataset:
| Prediction method | Details | File size limit |
|-------------------|---------|-----------------|
| Leaderboard predictions | To make predictions on a non-deployed model using the UI, expand the model on the Leaderboard and select [**Predict > Make Predictions**](predict). Upload predictions from a local file, URL, data source, or the AI Catalog. You can also upload predictions using the modeling predictions API, also called the "[V2 predictions API](https://datarobot-public-api-client.readthedocs-hosted.com/en/v2.26.1/entities/predict_job.html){ target=_blank }." Use this API to test predictions using your modeling workers on small datasets. Predictions can be limited to 100 requests per user, per hour, depending on your DataRobot package. | 1GB |
| Batch predictions (UI) | To make batch predictions using the UI, deploy a model and navigate to the deployment's  [**Make Predictions**](batch-pred) tab (requires MLOps). | 5GB |
| Batch predictions (API)  | The [Batch Prediction API](batch-prediction-api/index) is optimized for high-throughput and contains production grade connectivity options that allow you to not only push data through the API, but also connect to the AI catalog, cloud storage, databases, or data warehouses (requires MLOps). | Unlimited |
| Prediction API (real-time) | To make real-time predictions on a deployed model, use the [Prediction API](dr-predapi). | 50 MB |
---
title: Prefill application templates
description: Prefill applications upon creation to more easily visualize the end-user experience.
section_name: Apps
maturity: public-preview
---

# Prefill application templates {: #prefill-application-templates }

!!! info "Availability information"
    Prefilled No-Code AI App templates are off by default. Contact your DataRobot representative or administrator for information on enabling the feature.

    <b>Feature flags:</b> Enable Prefill NCA Templates with Training Data

Now available for public preview, you can choose whether or not to prefill application insights with training data, allowing you to more easily visualize the experience for the end-user. Previously, application widgets appeared blank until the app was used to make a prediction; now, with prefilled No-Code App templates enabled, the application can use the project's training data to populate the widgets.

After creating a new application, DataRobot exports the training dataset to the AI Catalog, and if the dataset is larger than 50,000 rows, performs random downsampling. Once registered, DataRobot then performs a batch prediction job by scoring the training data.  

![](images/app-prefill-3.png)

The **All Rows** widget populates using the training data as the prediction data source. Additionally, new tabs have been added so you can view training data, prediction data, or both.

![](images/app-prefill-1.png)

The **Predictions data** tab is only active after adding a batch prediction file or making a single-row prediction in the app.

!!! note "App prediction limit"
    The process of scoring the training data does not affect your application prediction limit.

## Set the prediction data source {: #set-the-prediction-data-source }

By default, the app uses only training data as the prediction data source. You can update the prediction data source in the app's settings.

To update the settings:

1. Click **Build** in the upper-right corner.
2. Click **Settings > General Configuration**. 
3. Under **Prediction Data Source** select a new option&mdash;Prediction Data Only, Training Data Only, or All Predictions Data.
    
    ![](images/app-prefill-2.png)

## Feature considerations {: #feature-considerations }

Prefilled templates are not available for time series applications.
# Microsoft Azure {: #microsoft-azure }

## Supported authentication {: #supported-authentication }

- Azure SQL Server/Synapse username/password
- Active Directory username/password

## Prerequisites {: #prerequisites }

The following is required before connecting to Azure in DataRobot:

- Azure SQL account

## Required parameters {: #required-parameters }

The table below lists the minimum required fields to establish a connection with Azure:

Required field | Description | Documentation
--------------- | ---------- | -----------
`address`       | The connection URL that supplies connection information for Azure.   |    [Microsoft documentation](https://docs.microsoft.com/en-us/sql/connect/jdbc/building-the-connection-url?view=azuresqldb-current){ target=_blank }

Learn about additional [configuration options for Azure](https://docs.microsoft.com/en-us/sql/connect/jdbc/setting-the-connection-properties?view=azuresqldb-current){ target=_blank }.

{% include 'includes/data-conn-trouble.md' %}
DataRobot returns the following error message when attempting to authenticate Azure credentials: <br><br>_Failed to authenticate. Check that this client has been granted access to your storage account and that your credentials are correct. Note that username/password authetnication only works with organizational accounts. It will not work with personal accounts._ | Check the user account type and make sure the user or group to which the user belongs is granted access via the asset's Access Control List (ACL).  | The user account must be in Microsoft Azure Active Directory (AD)&mdash;Service Principal is not supported.<br><br> If the user has an Azure AD account, [grant them access to the asset via its ACL](https://learn.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-access-control){ target=_blank }.
---
title: Notifications tab
description: Enable notifications, which trigger emails for service health and data drift reporting. Notifications are off by default but can be enabled by a deployment Owner. Configure Service Health, Data Drift, Accuracy, and Fairness monitoring.

---

# Notifications tab {: #notifications-tab }

DataRobot provides automated monitoring with a notification system. You can configure notifications to alert you when service health, data drift status, model accuracy, or fairness values exceed your defined acceptable levels. Notifications trigger emails. They are off by default but can be [enabled by a deployment _Owner_ in the deployment settings](deployment-settings/index). Keep in mind that notifications only control whether emails are sent to subscribers. If notifications are disabled, monitoring of service health, data drift, accuracy, and fairness statistics still occurs. You can also [schedule deployment reports](deploy-reports) on the **Notifications** tab.

!!! note 
    A deployment _Consumer_ only receives a notification when a deployment is shared with them and when a previously shared deployment is deleted. They are not notified about other events.

To set the types of notifications you want to receive:

1. In the **Deployments** inventory, open a deployment and click the **Notifications** tab.
    
2. Select whether to email notifications and, if so, whether to send them for all events or just critical events, then, click **Save**.

    ![](images/notify-1.png)

3. The monitoring actions are located on the [deployment settings](deployment-settings/index) pages, and your control over those settings depends on your [deployment role](roles-permissions#deployment-roles)&mdash;_Owner_ or _User_. Both roles can set personal **Notification Settings**; however, only deployment _Owners_ can set up schedules and thresholds to monitor the following:

    * [Service health](service-health-settings)

    * [Data drift status](data-drift-settings)

    * [Accuracy](accuracy-settings)

    * [Fairness](fairness-settings)

    Notifications are delivered as emails and must be set up for each deployment you want to monitor.

    ![](images/notify-3.png)

!!! tip
    You can also [schedule deployment reports](deploy-reports) on the **Notifications** tab.
---
dataset_name: 1k_diabetes-train.csv
expiration_date: 10-10-2024
owner: misha.yakubovskiy@datarobot.com
domain: core-modeling
title: Select a target
description: This tutorial provides instructions to select a prediction target for your project.
url: https://docs.datarobot.com/en/tutorials/creating-ai-models/tut-target.html

---

# Select a target {: #select-a-target }

In this tutorial, you'll learn how to select the target feature in a project.

The model building phase of the project begins with selecting a target feature, in other words, the column in your dataset that captures what you would like to predict. Once you select a target,  additional configuration options become available.

## Takeaways {: #takeaways }

This tutorial explains:

- How to select a target feature
- Available actions after target selection

## Select a target feature {: #select-a-target-feature }

You can begin building a project after importing a training dataset to DataRobot, which takes you to the **Data** tab.

1. Consider your use case, and begin typing the name of the target feature you would like to predict below **What would you like to predict**.

    ![](images/tut-target-1.png)

2. Select the target from the dropdown.

    ![](images/tut-target-2.png)

    ??? tip
        If you're unsure of the target feature name, scroll down to view a list of every feature in the training dataset.

    Once selected, DataRobot automatically analyzes the training dataset, determines the project type (classification if the dataset has categories or regression if the target is numerical), and displays the distribution of the target feature.

    The other **Start** screen configuration options also become available, including [modeling modes](model-ref#modeling-modes) and [advanced modeling options](adv-opt/index).

## Learn more {: #learn-more }

**Documentation:**

- [Configure advanced modeling options](adv-opt/index)
- [Select a modeling mode](model-ref#modeling-modes)
- [Set target feature in basic modeling workflow](model-data#set-the-target-feature)
- [Explore your data prior to model building](analyze-data/index)
---
title: Snowflake
description: Guides around integrating DataRobot with Snowflake

---

# Snowflake {: #snowflake }

The articles in this section progress from data ingest to creating projects and machine learning models based on historic training sets, and finally to scoring new data through deployed models via several deployment methodologies:

Topic | Describes...
----- | ------
[Data ingest](sf-project-creation) | Retrieving data for Snowflake project creation.
[Real-time predictions](sf-client-scoring) | Using the API to score Snowflake data.
[Server-side model scoring](sf-server-scoring) | Using the API to integrate your deployment with Snowflake to feed data to your model for predictions and to write those predictions back to your Snowflake Database.
[External functions and streams](sf-function-streams) | Using external API call functions to create a Snowflake scoring pipeline.
[Generate Snowflake UDF Scoring Code](snowflake-sc) | Using the DataRobot Scoring Code JAR as a user-defined function (UDF) on Snowflake.
---
title: No-Code AI App public preview features
description: Read preliminary documentation for app-related features currently in the DataRobot public preview pipeline.
section_name: Apps
maturity: public-preview
---

# No-Code AI App public preview features {: #no-code-ai-app-public-preview-features }

{% include 'includes/pub-preview-notice-include.md' %}

## Available application public preview documentation {: #available-application-public-preview-documentation }

=== "SaaS"

	Public preview for... | Describes...
	----- | ------
    [Prefilled app templates](app-prefill) | Prefill applications upon creation to more easily visualize the end-user experience.
    [Feature Discovery support in No-Code AI Apps](app-ft-cache)  | Create No-Code AI Apps from Feature Discovery projects.


=== "Self-Managed"

	Public preview for... | Describes...
	----- | ------
    [Prefilled app templates](app-prefill) | Prefill applications upon creation to more easily visualize the end-user experience.
---
title: Robot-to-Robot
description: DataRobot employees ask and answer question questions related to the platform and data science.
---

# Robot-to-Robot {: #robot-to-robot }

This section pulls back the curtain to reveal what DataRobot employees talk about in Slack. No surprises here&mdash;data science is still top of mind.

Use case | Description
-------- | -----------
[Model integrity and security](rr-model-integrity) | What measures does the platform support to assure the integrity and security of AI models?
[Single- vs. multi-tenant SaaS](rr-singlemulti-tenant.md) | What's the difference between the two deployment options with respect to DataRobot SaaS?
[ACE score and row order](rr-ace-score) | How is sampling done in EDA2 when calculating ACE scores?
[Redundant features explained](rr-redundant-features) | What makes a feature redundant?
[Import .tf or .keras?](rr-import-keras) | Have you looked at custom models?
[N-grams and prediction confidence](rr-n-gram-predictions) | What tools help understand n-gram predictions?
[GPU vs. CPU](rr-gpu-v-cpu) | What's the difference between GPUs and CPUs?
[Prediction Explanations on small data](rr-predex-small-data) | Can you get Prediction Explanations for a small dataset?
[Neural nets and tabular data](rr-nns-tabular-data) | Do you need neural networks for tabular data?
[PCA and K-Means clustering](rr-pca-kmeans) | What's the impact of PCA on KMeans?
[Default language change with Japanese](rr-language-nlp) | Why did the default language change when modeling Japanese text features?
[Let's talk target leakage](rr-target-leakage) | Why might target leakage show intermittently?
[Normalizing for monotonicity](rr-norm-for-mono) | Can you help me with monotonicity?
[An interesting way to use a payoff matrix](rr-payoff-matrix) | Consider justifying cost vs. identifying profit drivers.
[Target transforms](rr-target-transform) | How does transforming your target help ML models?
[Dynamic time warping](rr-dynamic-time) | What should I look out for with dynamic time warping?
[Multiple reduced feature lists](rr-reduced-feature-lists) | Can I have multiple Reduced Features lists for one project?
[NPS in DataRobot](rr-nps-scores) | Has anyone worked with net promoter scores (NPS)?
[Word Cloud repeats](rr-word-cloud) | Why would a term would show up multiple times in a word cloud?
[Ordered categoricals](rr-ordered-cat) | Does DataRobot recognize ordered categoricals?
[Offset/exposure with Gamma distributions](rr-offset-exposure-gamma) | How does DataRobot treat exposure and offset in model training with the target following a Gamma distribution?
---
title: Smart downsampling
description: Use smart downsampling to reduce the size of your output dataset when publishing a wrangling recipe.
section_name: Workbench
maturity: public-preview
---

# Publish recipes with smart downsampling {: #publish-recipes-with-smart-downsampling }

!!! info "Availability information"
     Smart downsampling in Workbench is off by default. Contact your DataRobot representative or administrator for information on enabling the feature.

     <b>Feature flag:</b> Enables Smart Downsampling in Wrangle Publishing Settings

You can use smart downsampling to reduce the size of your output dataset when publishing a wrangling recipe. Smart downsampling is a data science technique to reduce the time it takes to fit a model without sacrificing accuracy; it is particularly useful for imbalanced data. This downsampling technique accounts for class imbalance by stratifying the sample by class. In most cases, the entire minority class is preserved, and sampling only applies to the majority class. Because accuracy is typically more important on the minority class, this technique greatly reduces the size of the training dataset (reducing modeling time and cost), while preserving model accuracy.

To apply smart downsampling to a wrangling recipe:

1. [Wrangle a dataset](wb-add-operation), and when you've finished adding operations, click **Publish**.

     ![](images/wb-publish-5.png)

2. In the **Publish Settings** window, enable the **Automatic downsampling** toggle and click **Smart**.

     ![](images/wb-smart-down-1.png)

3. Select a **Target** feature&mdash;a binary classification or zero-inflated feature. If the dataset does not contain either feature type, the option to apply smart downsampling is unavailable. 

     ![](images/wb-smart-down-3.png)

4. (Optional) Enter a name for the **Weights feature**. This column, which contains downsampling weights, is computed and added to your output dataset as a result of smart downsampling.

5. Enter the desired **Maximum number of rows** _or_ **Estimated Size (MB)**. These values are linked, so if you change the value in one field, the other field updates automatically. See [DataRobot's dataset requirements](file-types) to ensure the output dataset is below the file size limit.

     ![](images/wb-smart-down-2.png)

6. When you're done [configuring publishing settings](wb-pub-recipe), click **Publish** in the upper-right corner.

!!! note 
     Any rows with `null` as a value in the target column will be filtered out after smart downsampling.
---
title: End-to-end ML workflow with Google Cloud Platform and BigQuery
description: Use Google Collaboratory to source data from BigQuery, build and evaluate a model using DataRobot, and deploy predictions from that model back into BigQuery and GCP.

---

# End-to-end ML workflow with Google Cloud Platform and BigQuery {: #end-to-end-ml-workflow-with-google-cloud-platform-and-bigquery }

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/end-to-end/GCP%20DataRobot%20End%20To%20End.ipynb){ .md-button }

DataRobot can integrate directly into your GCP environment, helping to accelerate your use of machine learning across all of the GCP services.

In this notebook accelerator, you can use Google Collaboratory or another notebook environment to source data from BigQuery, build and evaluate an ML model using DataRobot, and deploy predictions from that model back into BigQuery and GCP.

This accelerator covers the following:

1. **Prepare data and ensure connectivity:** In the first section of the notebook, you will load a sample dataset to be used for modeling into BigQuery. Once complete, you will connect your BigQuery data with DataRobot.

2. **Build and evaluate a model:** Using the DataRobot Python API, you will have DataRobot build close to 50 different machine learning models while also evaluating how those models perform on this dataset.

3. **Scoring and hosting:** In the final section, the entire dataset will be scored on the new model with prediction data written back to BigQuery for use in your GCP applications.
---
title: Sentiment analysis example
description: Apply DataRobot's Composable ML to capture sentiment from text.

---

# Sentiment analysis example {: #sentiment-analysis-example }

The model in this example includes reviews or tweets. The goal is to get an uplift for the model by capturing the sentiment in the text. To do this, simply [modify the blueprint](cml-blueprint-edit#access-the-blueprint-editor) (i.e., click **Copy and Edit** to start). The following is a simple blueprint.&mdash;text only&mdash;but the model could have features as well. 

![](images/cml-sentiment-example-1.png)

Hover over either the **Matrix of word-grams counts** or **Elastic-Net Classifier** nodes to see:

* The type of input required for that task.
* The type of output returned. 

For example, the **Matrix of word-grams counts** task requires the input to be of type Text and it returns a data frame with all numeric features:  

![](images/cml-sentiment-example-2.png)

To capture sentiments in a text feature for this example, hover over the **Text variables** node and click the [task selector](cml-blueprint-edit#use-the-task-selector) plus sign (![](images/icon-plus.png)). In the **Select a task** dialog box, expand **Preprocessing > Text Preprocessing** to see the options for text manipulations. (Some of these options are also available via [**Advanced Tuning**](adv-tuning), but others can only be accessed here.) Select to add **TextBlob Sentiment Featurizer**.

![](images/cml-sentiment-example-3.png)

The blueprint now shows a new node, outlined in red. When you hover over the node, you can see that it requires text:

![](images/cml-sentiment-example-4.png)

Note that the node's output is a data frame with numerical features (`Data Type: Numeric`). Because the **TextBlob Sentiment Featurizer** is a preprocessing module, you must [connect it to the model task](cml-blueprint-edit#work-with-nodes). (Hover over the TextBlob node, drag the diagonal arrow (![](images/icon-diagonal-arrow.png)) icon to the **Elastic-Net Classifier** node, and click.)

![](images/cml-sentiment-example-6.png)


The new blueprint is [ready to be trained](cml-blueprint-edit#train-new-models). (Before training, you can change the feature list or the training sample size.)

Here is the model on the Leaderboard, shown as one of the top four models. 

![](images/cml-sentiment-example-5.png)
---
title: Bias and Fairness resources
description: Provides links to bias and fairness resources used in DataRobot.

---

# Bias and Fairness resources {: #bias-and-fairness-resources }

The tools of the Bias and Fairness feature test your models for bias. This allows you identify bias before (or after) models are deployed and then to take action before the model's decisions cause negative outcomes for your organization. See a more complete overview [here](bias-overview).

The workflow for implementing bias and fairness is:

* Select one or more protected features and pick a fairness metric.
* Use insights to determine if models are biased with respect to the protected features.
* Monitor production models for bias.

The tools available for each step of working with Bias and Fairness are described in the following sections. Fairness metrics and terminology are described in the [Bias and Fairness reference](bias-ref).

Topic | Describes...
---|---
**Settings** | :~~:
[Advanced options: fairness metrics](fairness-metrics#set-fairness-metrics) | Set fairness metrics prior to model building (or [from the Leaderboard](fairness-metrics#configure-metrics-and-mitigation-post-autopilot) post-modeling).
[Advanced options: mitigation](fairness-metrics#set-mitigation-techniques) | Set mitigation techniques prior to model building (or [from the Leaderboard](fairness-metrics#configure-metrics-and-mitigation-post-autopilot) post-modeling).
**Model insights** | :~~:
[Per-Class Bias](per-class)  | Identify if a model is biased, and if so, how much and who it's biased towards or against.
[Cross-Class Data Disparity](cross-data) | Depict why a model is biased, and where in the training data it learned that bias from.
[Cross-Class Accuracy](cross-acc)  | Measure the model's accuracy for each class segment of the protected feature.
[Bias vs Accuracy](bias-tab) | View the tradeoff between predictive accuracy and fairness.
**Deployments** | :~~:
[Fairness monitoring](fairness-settings) | Configure tests that allow models to recognize, in real-time, when protected features in the dataset fail to meet predefined fairness conditions.
[Per-Class Bias](mlops-fairness#view-per-class-bias)  | Uses the fairness threshold and score of each class to determine if certain classes are experiencing bias in the model's predictive behavior.
[Fairness over time](mlops-fairness#view-fairness-over-time) | View how the distribution of a protected feature's fairness scores have changed over time.
**Reference** | :~~:
[Bias and Fairness overview](bias-overview) | View a brief overview and definition of bias and fairness, with links to further reading.
[Bias and Fairness reference](bias-ref) | Understand methods used to calculate fairness and to identify biases in the model's predictive behavior.